Covid19 Data

Import Data from Github

url_base <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/"

# Filenames for the specific datasets
filenames <- c("time_series_covid19_confirmed_global.csv",
               "time_series_covid19_deaths_global.csv",
               "time_series_covid19_recovered_global.csv")

# Constructing the full URLs to the raw CSV files
urls <- paste0(url_base, filenames)
names(urls) <- c("Confirmed", "Deaths", "Recovered")

# Reading in the datasets
datasets <- lapply(urls, read_csv, show_col_types = FALSE)
names(datasets) <- names(urls)

Data Tidying

# Tidy the datasets
tidy_datasets <- lapply(datasets, function(df) {
  # Sanitize the column names: replace '/' and ' ' with '.'
  names(df) <- make.names(names(df))

  # Convert the data from wide format to long format
  df %>%
    pivot_longer(
      cols = -matches("Province|Country|Lat|Long"),  # Use regular expression to exclude non-date columns
      names_to = "Date_Time",
      values_to = "Cases"
    ) %>%
    mutate(
      # Remove 'X' from column names if they exist
      Date_Time = gsub("^X", "", Date_Time),
      # Parse the date-time strings into Date objects
      Date = parse_date_time(Date_Time, orders = c("mdy HM", "mdy"))
    ) %>%
    # Convert the POSIXct to Date only
    mutate(Date = as.Date(Date)) %>%
    # Drop unnecessary columns
    select(-c(Lat, Long, Date_Time))
})

#Viz 1

confirmed_cases <- tidy_datasets$Confirmed

# Create a summary of total cases by date
cases_by_date <- confirmed_cases %>%
  group_by(Date) %>%
  summarize(TotalCases = sum(Cases, na.rm = TRUE), .groups = "drop")

# Generate the heatmap
ggplot(cases_by_date, aes(x = Date, y = 1, fill = TotalCases)) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = "red") +
  labs(title = "Heatmap of COVID-19 Cases Over Time", x = "Date", y = "", fill = "Total Cases") +
  theme_minimal() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank(),
        legend.position = "bottom")

Heatmap of COVID-19 cases over time

Visualization 2: Bar Chart Comparing Cases, Deaths, and Recoveries

A bar chart that compares the total cases, deaths, and recoveries across the top affected countries gives a quick visual summary of the situation.

confirmed_cases <- tidy_datasets$Confirmed
deaths <- tidy_datasets$Deaths
recovered <- tidy_datasets$Recovered

# Summarize the latest total cases, deaths, and recoveries by country
latest_totals <- confirmed_cases %>%
  group_by(`Country.Region`) %>%
  summarize(LatestConfirmed = max(Cases), .groups = "drop") %>%
  left_join(deaths %>%
              group_by(`Country.Region`) %>%
              summarize(LatestDeaths = max(Cases), .groups = "drop"), by = "Country.Region") %>%
  left_join(recovered %>%
              group_by(`Country.Region`) %>%
              summarize(LatestRecovered = max(Cases), .groups = "drop"), by = "Country.Region")

# Top 10 affected countries
top_countries <- latest_totals %>%
  arrange(desc(LatestConfirmed)) %>%
  slice_head(n = 10)

# Create the bar chart
ggplot(top_countries, aes(x = reorder(`Country.Region`, -LatestConfirmed), y = LatestConfirmed)) +
  geom_bar(stat = "identity", aes(fill = "Confirmed"), position = "dodge") +
  geom_bar(stat = "identity", aes(y = LatestDeaths, fill = "Deaths"), position = "dodge") +
  geom_bar(stat = "identity", aes(y = LatestRecovered, fill = "Recovered"), position = "dodge") +
  scale_fill_manual(values = c("Confirmed" = "blue", "Deaths" = "red", "Recovered" = "green")) +
  labs(title = "COVID-19 Cases, Deaths, and Recoveries in Top 10 Affected Countries",
       x = "Country", y = "Count", fill = "Type") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  coord_flip()  # Flip coordinates for horizontal bars

Comparison of COVID-19 cases, deaths, and recoveries

#Viz #3 Bubble Chart

Interactive bubble chart of COVID-19 impact

confirmed_cases <- tidy_datasets$Confirmed

# Create a summary of total cases by date globally
global_cases_summary <- confirmed_cases %>%
  group_by(Date) %>%
  summarize(TotalGlobalCases = sum(Cases, na.rm = TRUE), .groups = "drop")

# Create a numerical representation of the date for the linear model
global_cases_summary <- global_cases_summary %>%
  mutate(DaysSinceFirstCase = as.numeric(Date - min(Date)))

# Build the linear model
model <- lm(TotalGlobalCases ~ DaysSinceFirstCase, data = global_cases_summary)

# Create a dataframe for future predictions
future_dates <- data.frame(DaysSinceFirstCase = as.numeric(seq(from = max(global_cases_summary$DaysSinceFirstCase),
                                                               to = max(global_cases_summary$DaysSinceFirstCase) + 30,
                                                               by = 1)))

# Predict future cases
predictions <- predict(model, newdata = future_dates)

# Add predictions to the original data
future_dates$PredictedCases <- predictions
future_dates$Date <- min(global_cases_summary$Date) + future_dates$DaysSinceFirstCase

# Visualize the actual data along with the forecast
ggplot(global_cases_summary, aes(x = Date, y = TotalGlobalCases)) +
  geom_line(color = "blue", linewidth = 1) +
  geom_line(data = future_dates, aes(x = Date, y = PredictedCases), color = "red", linewidth = 1, linetype = "dashed") +
  labs(title = "Forecasting COVID-19 Cases with Linear Regression",
       x = "Date",
       y = "Total Global Cases") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_x_date(date_breaks = "1 month", date_labels = "%b %Y")

Linear Model Forecast of COVID-19 Cases

Bias and conclusion

To wrap up, the charts and models we’ve shown give a quick look at how COVID-19 has evolved and affected us. But, it’s important to remember that there might be gaps and biases in our data and how we’ve analyzed it.

Our data comes from public sources, and it’s not perfect. Different places report their numbers differently, and not all cases or deaths get counted the same everywhere. This means we might not be seeing the full picture of the pandemic’s impact.

The forecasting model we used, linear regression, is pretty basic. It assumes that the virus spreads at a constant rate, which overlooks the real-world complexities of how diseases spread and how actions like lockdowns or mask-wearing can change things.

Also, our visuals don’t show how the virus affects people differently based on the size of their population or consider important details like how many people recover or die from the virus. These aspects are key to truly understanding the depth and severity of COVID-19.

So, when looking at COVID-19 data, it’s vital to keep these limitations in mind. Moving forward, we should use more detailed models and draw from a wider range of data to get a clearer and more accurate picture of the pandemic. ```